Currently, wireless transmission is a key information sharing method due to their wireless radio propagation nature. Among wireless technologies, cognitive radio networks (CRNs) have become a promising platforms due to their capabilities of exploring any unused radio spectrum for information transmission. CRN is a type of spectrum-agile wireless network. Spectrum sensing can be a function on the front end of the cognitive radio (CR) system and can be designed to detect a primary user's signals. It can be used to detect and identify data traffic patterns of licensed (or primary) users (PUs)'s using spectrum sensing and classification, as the secondary users (SUs) of a CRN are not supposed to interfere with the PUs' communications.
In certain implementations, spectrum sensing schemes can be used to evaluate the signal features in the cyclostationary domain in CRNs. The cyclostationary (cyclic) feature detector can detect and classify the PU signals impaired by noise and interference with relatively high accuracy. However, general cyclic spectrum detector may require a high sampling rate and can impose a heavy computational load to the system. In some situations, compressive sensing (CS) can be used with the CR spectrum sensing in order to reduce the computational load by using a low sub-Nyquist sampling rate to collect the compressed measurements. However, CS methods still have a high computation overload due to their complicated signal recovery algorithms.
The presently disclosed systems and methods for detecting unused communication spectrum are directed to overcoming one or more of the problems set forth above and/or other problems in the art.